Navigating challenges in pediatric trial conduct: integrating bayesian sequential design with semiparametric elicitation for handling primary and secondary endpoints
Abstract Background This study presents a Bayesian Adaptive Semiparametric approach designed to address the challenges of pediatric randomized controlled trials (RCTs). The study focuses on efficiently handling primary and secondary endpoints, a critical aspect often overlooked in pediatric trials....
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BMC
2025-03-01
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| Online Access: | https://doi.org/10.1186/s12874-025-02484-7 |
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| author | Danila Azzolina Ileana Baldi Silvia Bressan Mohd Rashid Khan Liviana Da Dalt Dario Gregori Paola Berchialla |
| author_facet | Danila Azzolina Ileana Baldi Silvia Bressan Mohd Rashid Khan Liviana Da Dalt Dario Gregori Paola Berchialla |
| author_sort | Danila Azzolina |
| collection | DOAJ |
| description | Abstract Background This study presents a Bayesian Adaptive Semiparametric approach designed to address the challenges of pediatric randomized controlled trials (RCTs). The study focuses on efficiently handling primary and secondary endpoints, a critical aspect often overlooked in pediatric trials. This methodology is particularly pertinent in scenarios where sparse or conflicting prior data are present, a common occurrence in pediatric research, particularly for rare diseases or conditions. Method Our approach considers Bayesian adaptive design, enhanced with B-Spline Semiparametric priors, allowing for the dynamic updating of priors with ongoing data. This improves the efficiency and accuracy of the treatment effect estimation. The Semiparametric prior inherent flexibility makes it suitable for pediatric populations, where responses to treatment can be highly variable. The design operative characteristics were assessed through a simulation study, motivated by the real-world case of the REnal SCarring Urinary infEction Trial (RESCUE). Result We demonstrate that Semiparametric prior parametrization exhibits an improved tendency to correctly declare the treatment effect at the study conclusion, even if recruitment challenges, uncertainty, and prior-data conflict arise. Moreover, the Semiparametric prior design demonstrates an improved ability in truly stopping for futility, with this tendency varying with the sample size and discontinuation rates. Approaches based on Parametric priors are more effective in detecting treatment efficacy during interim assessments, particularly with larger sample sizes. Conclusion Our findings indicate that these methods are especially effective in managing the complexities of pediatric trials, where prior data may be limited or contradictory. The flexibility of Semiparametric prior design in incorporating new evidence proves advantageous in addressing recruitment challenges and making informed decisions with restricted data. |
| format | Article |
| id | doaj-art-16bd99c6b2e94d17b45c2d268bb68d5e |
| institution | DOAJ |
| issn | 1471-2288 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Research Methodology |
| spelling | doaj-art-16bd99c6b2e94d17b45c2d268bb68d5e2025-08-20T03:07:43ZengBMCBMC Medical Research Methodology1471-22882025-03-0125111110.1186/s12874-025-02484-7Navigating challenges in pediatric trial conduct: integrating bayesian sequential design with semiparametric elicitation for handling primary and secondary endpointsDanila Azzolina0Ileana Baldi1Silvia Bressan2Mohd Rashid Khan3Liviana Da Dalt4Dario Gregori5Paola Berchialla6Department of Environmental and Preventive Science, University of FerraraUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of PadovaDepartment of Women’s and Children’s Health, University of PadovaUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of PadovaDepartment of Women’s and Children’s Health, University of PadovaUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of PadovaDepartment of Clinical and Biological Science, University of TurinAbstract Background This study presents a Bayesian Adaptive Semiparametric approach designed to address the challenges of pediatric randomized controlled trials (RCTs). The study focuses on efficiently handling primary and secondary endpoints, a critical aspect often overlooked in pediatric trials. This methodology is particularly pertinent in scenarios where sparse or conflicting prior data are present, a common occurrence in pediatric research, particularly for rare diseases or conditions. Method Our approach considers Bayesian adaptive design, enhanced with B-Spline Semiparametric priors, allowing for the dynamic updating of priors with ongoing data. This improves the efficiency and accuracy of the treatment effect estimation. The Semiparametric prior inherent flexibility makes it suitable for pediatric populations, where responses to treatment can be highly variable. The design operative characteristics were assessed through a simulation study, motivated by the real-world case of the REnal SCarring Urinary infEction Trial (RESCUE). Result We demonstrate that Semiparametric prior parametrization exhibits an improved tendency to correctly declare the treatment effect at the study conclusion, even if recruitment challenges, uncertainty, and prior-data conflict arise. Moreover, the Semiparametric prior design demonstrates an improved ability in truly stopping for futility, with this tendency varying with the sample size and discontinuation rates. Approaches based on Parametric priors are more effective in detecting treatment efficacy during interim assessments, particularly with larger sample sizes. Conclusion Our findings indicate that these methods are especially effective in managing the complexities of pediatric trials, where prior data may be limited or contradictory. The flexibility of Semiparametric prior design in incorporating new evidence proves advantageous in addressing recruitment challenges and making informed decisions with restricted data.https://doi.org/10.1186/s12874-025-02484-7Pediatric trialSemiparametric priorPrior ElicitationMultiple endpointsSequential designBayesian trial |
| spellingShingle | Danila Azzolina Ileana Baldi Silvia Bressan Mohd Rashid Khan Liviana Da Dalt Dario Gregori Paola Berchialla Navigating challenges in pediatric trial conduct: integrating bayesian sequential design with semiparametric elicitation for handling primary and secondary endpoints BMC Medical Research Methodology Pediatric trial Semiparametric prior Prior Elicitation Multiple endpoints Sequential design Bayesian trial |
| title | Navigating challenges in pediatric trial conduct: integrating bayesian sequential design with semiparametric elicitation for handling primary and secondary endpoints |
| title_full | Navigating challenges in pediatric trial conduct: integrating bayesian sequential design with semiparametric elicitation for handling primary and secondary endpoints |
| title_fullStr | Navigating challenges in pediatric trial conduct: integrating bayesian sequential design with semiparametric elicitation for handling primary and secondary endpoints |
| title_full_unstemmed | Navigating challenges in pediatric trial conduct: integrating bayesian sequential design with semiparametric elicitation for handling primary and secondary endpoints |
| title_short | Navigating challenges in pediatric trial conduct: integrating bayesian sequential design with semiparametric elicitation for handling primary and secondary endpoints |
| title_sort | navigating challenges in pediatric trial conduct integrating bayesian sequential design with semiparametric elicitation for handling primary and secondary endpoints |
| topic | Pediatric trial Semiparametric prior Prior Elicitation Multiple endpoints Sequential design Bayesian trial |
| url | https://doi.org/10.1186/s12874-025-02484-7 |
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